February 22nd, 2022 
Daniela Lopez de Luise Title: Generation and Detection of Deepfakes
Speaker: Antitza Dantcheva (INRIA, France)
To join the free 1-hour webinar, it is required to pre-register at,
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or through the journal website at,
https://jivp-eurasipjournals.springeropen.com/
Contact: {jana.palinkas, esinu.abadjivor}@springernature.com
Abstract: Generative adversarial networks have made remarkable progress in generating realistic images of high quality. While video generation is the natural sequel, it entails a number of challenges w.r.t. complexity and computation, associated to the simultaneous modeling of appearance, as well as motion. I will talk about our work related to design of generative models, which allow for realistic generation of face images and videos. We have placed emphasis on disentangling motion from appearance and have learned motion representations directly from RGB, without structural representations such as facial landmarks or 3D meshes. In our latest work, we have aimed at constructing motion as linear displacement of codes in the latent space. Based on this, our model LIA (Latent Image Animator) is able to animate images via navigation in the latent space. While highly intriguing, video generation has thrusted upon us the imminent danger of deepfakes, which can offer unprecedented levels of increasingly realistic manipulated videos. Deepfakes pose an imminent security threat to us all, and to date, deepfakes are able to mislead face recognition systems, as well as humans. Hence, we design generation and detection methods in parallel. In the second part of my talk, I will discuss our associated work, where in our latest work, we explore attention mechanisms in 3D CNNs.
Short bio: Antitza Dantcheva is a Research Scientist with the STARS team of INRIA Sophia Antipolis, France. Previously, she was a Marie Curie fellow at Inria and a Postdoctoral Fellow at the Michigan State University and the West Virginia University, USA. She received her Ph.D. degree from Telecom ParisTech/Eurecom in image processing and biometrics in 2011. Her research is in computer vision and specifically in designing algorithms that seek to learn suitable representations of the human face in interpretation and generation. She is recipient among others of the prestigious ANR Jeunes chercheuses / Jeunes chercheurs (JCJC) personal grant, the Best Poster Award at IEEE FG 2019, winner of the Bias Estimation in Face Analytics (BEFA) Challenge at ECCV 2018 (in the team with Abhijit Das and Francois Bremond) and Best Paper Award (Runner up) at the IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2017).
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